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. 2025 Aug 20:JCO2402681.
doi: 10.1200/JCO-24-02681. Online ahead of print.

Deep Learning Model for Breast Shear Wave Elastography to Improve Breast Cancer Diagnosis (INSPiRED 006): An International, Multicenter Analysis

Lie Cai  1 , André Pfob  1   2   3 , Richard G Barr  4 , Volker Duda  5 , Zaher Alwafai  6 , Corinne Balleyguier  7 , Dirk-André Clevert  8 , Sarah Fastner  3 , Christina Gomez  3 , Manuela Goncalo  9 , Ines Gruber  10 , Markus Hahn  10 , Panagiotis Kapetas  11   12 , Juliane Nees  3 , Ralf Ohlinger  6 , Fabian Riedel  1 , Matthieu Rutten  13   14 , Anne Stieber  1 , Riku Togawa  1 , Chris Sidey-Gibbons  15 , Mitsuhiro Tozaki  16 , Sebastian Wojcinski  17 , Joerg Heil  1   3 , Michael Golatta  1   3
Affiliations

Deep Learning Model for Breast Shear Wave Elastography to Improve Breast Cancer Diagnosis (INSPiRED 006): An International, Multicenter Analysis

Lie Cai et al. J Clin Oncol. .

Abstract

Purpose: Shear wave elastography (SWE) has been investigated as a complement to B-mode ultrasound for breast cancer diagnosis. Although multicenter trials suggest benefits for patients with Breast Imaging Reporting and Data System (BI-RADS) 4(a) breast masses, widespread adoption remains limited because of the absence of validated velocity thresholds. This study aims to develop and validate a deep learning (DL) model using SWE images (artificial intelligence [AI]-SWE) for BI-RADS 3 and 4 breast masses and compare its performance with human experts using B-mode ultrasound.

Methods: We used data from an international, multicenter trial (ClinicalTrials.gov identifier: NCT02638935) evaluating SWE in women with BI-RADS 3 or 4 breast masses across 12 institutions in seven countries. Images from 11 sites were used to develop an EfficientNetB1-based DL model. An external validation was conducted using data from the 12th site. Another validation was performed using the latest SWE software from a separate institutional cohort. Performance metrics included sensitivity, specificity, false-positive reduction, and area under the receiver operator curve (AUROC).

Results: The development set included 924 patients (4,026 images); the external validation sets included 194 patients (562 images) and 176 patients (188 images, latest SWE software). AI-SWE achieved an AUROC of 0.94 (95% CI, 0.91 to 0.96) and 0.93 (95% CI, 0.88 to 0.98) in the two external validation sets. Compared with B-mode ultrasound, AI-SWE significantly reduced false-positive rates by 62.1% (20.4% [30/147] v 53.8% [431/801]; P < .001) and 38.1% (33.3% [14/42] v 53.8% [431/801]; P < .001), with comparable sensitivity (97.9% [46/47] and 97.8% [131/134] v 98.1% [311/317]; P = .912 and P = .810).

Conclusion: AI-SWE demonstrated accuracy comparable with human experts in malignancy detection while significantly reducing false-positive imaging findings (ie, unnecessary biopsies). Future studies should explore its integration into multimodal breast cancer diagnostics.

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